Aiming at the formation control problem of multiple quadrotor unmanned aerial vehicles (UAVs) subject to input saturation and actuator faults, this paper proposes a finite-time fault-tolerant control strategy based on the command-filtered backstepping. UAV systems operating in complex real-world environments face multiple challenges, including actuator faults with nonlinear coupling characteristics due to factors such as mechanical wear, control input saturation caused by physical power limitations of components, as well as parametric uncertainties and external disturbances, which severely affect the reliability, robustness, and transient performance of formation control. To tackle these challenges, this study focuses on the finite-time leader-follower formation tracking control problem under the coupling influence of the aforementioned multiple complex factors. Specifically, a finite-time command filter is introduced to resolve the issue of high computational complexity arising from repeated differentiation in conventional backstepping design, along with a compensation mechanism to eliminate filtering errors. Secondly, for unknown nonlinear dynamics in the system, radial basis function neural networks are employed for approximation, with an adaptive law designed to update the weights online. Additionally, an anti-windup compensator is constructed to mitigate the adverse effects of input saturation. Based on these designs, the finite-time stability of the closed-loop system is rigorously analyzed using the finite-time Lyapunov stability criterion, demonstrating the finite-time boundedness of all closed-loop signals. Finally, the effectiveness of the proposed control algorithm is validated through two sets of comparative simulation results. The results indicate that, even under the combined influence of actuator faults and input saturation constraints, the proposed algorithm ensures that the UAV formation tracks the desired trajectory quickly and accurately within a finite time, while exhibiting excellent fault-tolerant capability, robustness, and transient performance. This work provides an effective solution for high-precision and highly reliable cooperative control of UAV swarms in complex uncertain environments.
WANG et al. (Sun,) studied this question.